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I am trying to reproduce this chart:

enter image description here

which depicts the probability of various a gene is insert in a mutant screen by a transposon. Apparently the graph is constructed from the following equation:

enter image description here

where Pi s the probability of finding one insert within a given gene, x is the average length of a gene transcript (1.7 kb), y is the total length of transcribed region in the gene space (113.1 Mb), Rtr is the probability of insertion into a transcription region within the gene space (0.573), Rgs is the probability of insertion into gene space in the whole genome (0.786), and N is the total number of Tnt1 inserts required.

However when I try and use this equation to generate the data, say for a 1.7Kbp gene I get this:

N <- seq(1, 20, 1) 
P <- 1 - (1 - ( 1.7 / 113.1 )) ^ (0.573 * 0.786 * N)
plot(N, P, type = "l")

enter image description here

Where the values are nothing like the chart. I have tried converting the kb and mb values to the same units but this did not help. I feel that I must be interpreting the equation wrong and must be super obvious and staring me in the face but I just cant see it.

To be clear, I am fine with recreating the aesthetic effects I just need help regenerating the data.

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  • $\begingroup$ The notation is too small for me to tell, but I think N goes from 1 to 9*10^3, not from 1 to 20. Which if you plot that for the x-axis range the chart looks closer to the one you've posted. $\endgroup$ – Andy W Jun 26 '15 at 4:57
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Andy W is correct.

I misunderstood the x-axis scale. Its actually 10^5, but I thought the exponential only applied to the last value on the x-axis to represent a saturation of transposons converging on prob = 1. To kind of demonstrate the asymptote.

Here is a demonstration that it is in fact correct.

N <- seq(1, 9e+05, 1) 
P <- 1 - (1 - ( 0.5 / 113.1 )) ^ (0.573 * 0.786 * N)
q <- 1 - (1 - ( 5 / 113.1 )) ^ (0.573 * 0.786 * N)
r <- 1 - (1 - ( 1.5 / 113.1 )) ^ (0.573 * 0.786 * N)
plot(N[1:5000], P[1:5000], type = "l")
lines(N[1:5000], q[1:5000], col = "red")
lines(N[1:5000], r[1:5000], col = "blue")

enter image description here

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